Baselines. Because there are not many existing learning-based implementations for LiDAR-based MOS available, we only choose three typical approaches using different types of inputs as representatives.
(1) Range Image View: LMNet [1]} uses the residual images as additional channels together with the range images as input to a range image backbone and is trained with binary labels. A kNN post-processing [2]} is used to reduce artifacts on objects' borders. And we choose the best setting of LMNet (with SalsaNext backbone) for comparison.
(2) Bird’s Eye View (BEV): LiMoSeg [3]} uses two successive LiDAR scans in 2D BEV representation to perform pixel-wise classification and can run at high frame rates on embedded platforms.
(3) Point-Voxel View: Cylinder3D [4]} uses cylindrical partition and point-level feature extractor to segmentation. We modify its open source codehttps://github.com/xinge008/Cylinder3D to input two consecutive aligned frames and train it from scratch with MOS-labels to perform MOS.
